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1
Žák, Marek, Jaroslav Rozman, and František V. Zbořil. "Design and Control of 7-DOF Omni-directional Hexapod Robot." Open Computer Science 11, no. 1 (December 17, 2020): 80–89. http://dx.doi.org/10.1515/comp-2020-0189.
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AbstractLegged robots have great potential to travel across various types of terrain. Their many degrees of freedom enable them to navigate through difficult terrains, narrow spaces or various obstacles and they can move even after losing a leg. However, legged robots mostly move quite slowly. This paper deals with the design and construction of an omni-directional seven degrees of freedom hexapod (i.e., six-legged) robot, which is equipped with omnidirectional wheels (two degrees of freedom are used, one for turning the wheel and one for the wheel itself) usable on flat terrain to increase travel speed and an additional coxa joint that makes the robot more robust when climbing inclined terrains. This unique combination of omnidirectional wheels and additional coxa joint makes the robot not only much faster but also more robust in rough terrains and allows the robot to ride inclined terrains up to 40 degrees and remain statically stable in slopes up to 50 degrees. The robot is controlled by a terrain adaptive movement controller which adjusts the movement speed and the gait of the robot according to terrain conditions.
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ZHANG, HE, RUI WU, CHANGLE LI, XIZHE ZANG, YANHE ZHU, HONGZHE JIN, XUEHE ZHANG, and JIE ZHAO. "ADAPTIVE MOTION PLANNING FOR HITCR-II HEXAPOD ROBOT." Journal of Mechanics in Medicine and Biology 17, no. 07 (November 2017): 1740040. http://dx.doi.org/10.1142/s0219519417400401.
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Multi-legged robots have the ability to traverse rugged terrain and can surmount the obstacles, which are impossible for being overcome by wheeled robots. In this regard, six-legged (hexapod) robots are considered to provide the best combination of adequate adaptability and control complexity. Their motion planning envisages calculating sequences of footsteps and body posture, accounting for the influence of terrain shape, in order to produce the appropriate foot-end trajectory and ensure stable and flexible motion of hexapod robots on the rugged terrain. In this study, a high-order polynomial is used to describe the trajectory model, and a new motion planning theory is proposed, which is aimed at the adaptation of hexapod robots to more complex terrains. An attempt is made to elaborate the adaptive motion planning and perform its experimental verification for a novel hexapod robot HITCR-II, demonstrating its applicability for walking on the unstructured terrain.
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Hao, Qian, Zhaoba Wang, Junzheng Wang, and Guangrong Chen. "Stability-Guaranteed and High Terrain Adaptability Static Gait for Quadruped Robots." Sensors 20, no. 17 (August 31, 2020): 4911. http://dx.doi.org/10.3390/s20174911.
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Stability is a prerequisite for legged robots to execute tasks and traverse rough terrains. To guarantee the stability of quadruped locomotion and improve the terrain adaptability of quadruped robots, a stability-guaranteed and high terrain adaptability static gait for quadruped robots is addressed. Firstly, three chosen stability-guaranteed static gaits: intermittent gait 1&2 and coordinated gait are investigated. In addition, then the static gait: intermittent gait 1, which is with the biggest stability margin, is chosen to do a further research about quadruped robots walking on rough terrains. Secondly, a position/force based impedance control is employed to achieve a compliant behavior of quadruped robots on rough terrains. Thirdly, an exploratory gait planning method on uneven terrains with touch sensing and an attitude-position adjustment strategy with terrain estimation are proposed to improve the terrain adaptability of quadruped robots. Finally, the proposed methods are validated by simulations.
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Cruz Ulloa, Christyan, Lourdes Sánchez, Jaime Del Cerro, and Antonio Barrientos. "Deep Learning Vision System for Quadruped Robot Gait Pattern Regulation." Biomimetics 8, no. 3 (July 3, 2023): 289. http://dx.doi.org/10.3390/biomimetics8030289.
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Robots with bio-inspired locomotion systems, such as quadruped robots, have recently attracted significant scientific interest, especially those designed to tackle missions in unstructured terrains, such as search-and-rescue robotics. On the other hand, artificial intelligence systems have allowed for the improvement and adaptation of the locomotion capabilities of these robots based on specific terrains, imitating the natural behavior of quadruped animals. The main contribution of this work is a method to adjust adaptive gait patterns to overcome unstructured terrains using the ARTU-R (A1 Rescue Task UPM Robot) quadruped robot based on a central pattern generator (CPG), and the automatic identification of terrain and characterization of its obstacles (number, size, position and superability analysis) through convolutional neural networks for pattern regulation. To develop this method, a study of dog gait patterns was carried out, with validation and adjustment through simulation on the robot model in ROS-Gazebo and subsequent transfer to the real robot. Outdoor tests were carried out to evaluate and validate the efficiency of the proposed method in terms of its percentage of success in overcoming stretches of unstructured terrains, as well as the kinematic and dynamic variables of the robot. The main results show that the proposed method has an efficiency of over 93% for terrain characterization (identification of terrain, segmentation and obstacle characterization) and over 91% success in overcoming unstructured terrains. This work was also compared against main developments in state-of-the-art and benchmark models.
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Hashimoto, Kenji, Yusuke Sugahara, Hun-Ok Lim, and Atsuo Takanishi. "Biped Landing Pattern Modification Method and Walking Experiments in Outdoor Environment." Journal of Robotics and Mechatronics 20, no. 5 (October 20, 2008): 775–84. http://dx.doi.org/10.20965/jrm.2008.p0775.
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Many researchers have studied walking stability control for biped robots, most of which involve highly precise acceleration controls based on robot model mechanics. Modeling error, however, makes the control algorithms used difficult to apply to biped walking robots intended to transport human users. The “landing pattern modification method” we propose is based on nonlinear admittance control. Theoretical compliance displacement calculated from walking patterns is compared to actual compliance displacement, when a robot's foot contacts slightly uneven terrain. Terrain height is detected and the preset walking pattern is modified accordingly. The new biped foot we also propose forms larger support polygons on uneven terrain than conventional biped foot systems do. Combining our new modification method and foot, a human-carrying biped robot can traverse uneven terrain, as confirmed in walking experiments.
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Chen, Yang, Yao Wu, Wei Zeng, and Shaoyi Du. "Kinematics Model Estimation of 4W Skid-Steering Mobile Robots Using Visual Terrain Classification." Journal of Robotics 2023 (October 11, 2023): 1–12. http://dx.doi.org/10.1155/2023/1632563.
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Accurate real-time kinematics model is very important for the control of a skid-steering mobile robot. In this study, the kinematics model of the skid-steering mobile robots was first designed based on instantaneous rotation centers (ICRs). Then, the extended Kalman filter (EKF) technique was applied to obtain the parameters of ICRs under the same specific terrain online. To adapt to different terrain environments, the fractal dimension-based SFTA (segmentation-based fractal texture analysis) method was used to extract features of different terrains, and the k-nearest neighbor (KNN) method was used to classify the terrains. In the case of real-time terrain recognition, the filter parameters of the EKF for estimating the ICRs are adjusted adaptively. Experiments on a real skid-steering mobile robot show that this method can quickly estimate the kinematics model of the robot in the case of terrain changes, and can meet the needs of practical applications. The average error of odometer estimation based on visual terrain classification is 0.06 m, while the average error of odometer estimation without terrain classification is 0.14 m.
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Pecie, Robert Florian, Mihai Olimpiu Tătar, and Călin Rusu. "Studies on mobile robots for all types of terrain." MATEC Web of Conferences 343 (2021): 08015. http://dx.doi.org/10.1051/matecconf/202134308015.
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In the first part of the paper, the authors present the characteristics of the robots for all types of terrain. In the second part, two categories of robots are proposed: a robot with hybrid locomotion system and a modular robot. For the last category, if different modules are combined, a family of modular robots adaptable to different types of terrain can be obtained. The solutions proposed by the authors allow the study of the mobility and adaptability of robots to different types of terrain.
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Huang, Han, Yu Feng, Xiong Yang, Liu Yang, and Yajing Shen. "An Insect-Inspired Terrains-Adaptive Soft Millirobot with Multimodal Locomotion and Transportation Capability." Micromachines 13, no. 10 (September 22, 2022): 1578. http://dx.doi.org/10.3390/mi13101578.
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Inspired by the efficient locomotion of insects in nature, researchers have been developing a diverse range of soft robots with simulated locomotion. These robots can perform various tasks, such as carrying medicines and collecting information, according to their movements. Compared to traditional rigid robots, flexible robots are more adaptable and terrain-immune and can even interact safely with people. Despite the development of biomimetic principles for soft robots, how their shapes, morphology, and actuation systems respond to the surrounding environments and stimuli still need to be improved. Here, we demonstrate an insect-scale soft robot with multi-locomotion modes made by Ecoflex and magnetic particles, which can be actuated by a magnetic field. Our robot can realize four distinct gaits: horizontal tumbling for distance, vertical tumbling for height, imitation of gastropod writhing, and inchworm-inspired crawling for cargo delivery. The soft compliant structure and four locomotion modes make the robot ideal for maneuvering in congested or complex spaces. In addition to linear motion (~20 mm/s) and turning (50°/s) on a flat terrain, the robot can also maneuver on various surface conditions (such as gaps, smooth slopes, sand, muddy terrain, and water). These merits, together with the robot’s high load-carrying capacity (5 times its weight), low cost, obstacle-crossing capability (as high as ~50% its length), and pressure resistance (70 kg), allow for a wide variety of applications.
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Li, Daxian, Wu Wei, and Zhiying Qiu. "Combined Reinforcement Learning and CPG Algorithm to Generate Terrain-Adaptive Gait of Hexapod Robots." Actuators 12, no. 4 (April 3, 2023): 157. http://dx.doi.org/10.3390/act12040157.
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Terrain adaptation research can significantly improve the motion performance of hexapod robots. In this paper, we propose a method that combines reinforcement learning with a central pattern generator (CPG) to enhance the terrain adaptation of hexapod robots in terms of gait planning. The hexapod robot’s complex task presents a high-dimensional observation and action space, which makes it challenging to directly apply reinforcement learning to robot control. Therefore, we utilize the CPG algorithm to generate the rhythmic gait while compressing the action space dimension of the agent. Additionally, the proposed method requires less internal sensor information, which exhibits strong applicability. Finally, we conduct experiments and deploy the proposed framework in the simulation environment. The results show that the terrain adaptation policy trained in our framework enables the hexapod robot to move more smoothly and efficiently on rugged terrain compared to the traditional CPG method.
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Li, Xu, Songyuan Zhang, Haitao Zhou, Haibo Feng, and Yili Fu. "Locomotion Adaption for Hydraulic Humanoid Wheel-Legged Robots Over Rough Terrains." International Journal of Humanoid Robotics 18, no. 01 (February 2021): 2150001. http://dx.doi.org/10.1142/s0219843621500018.
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Compared with the traditional hydraulic humanoid robots, the WLR-II, a novel hydraulic wheel-legged robot developed by using hose-less design, can significantly increase the reliability and maneuverability. The WLR-II combines the rough-terrain capability of legs with the efficiency of wheels. In this paper, a novel framework called rough-terrain adaption framework (RTAF) is presented which allows WLR-II to move on both flat terrains and terrains with unmodeled contact dynamics. RTAF is a hierarchical framework, which has a high-level balance controller and a low-level impedance controller that a high-performance nested torque controller with feed-forward velocity compensation is used. The low-level impedance controller for the hydraulic-driven unit can cancel out the load dynamics influence such as unexpected terrain disturbances and increase the force-tracking performance. With the high-level balance controller, the robot is able to handle unexpected terrain disturbances through wheel-ground force estimation, pitch/roll balance control and impedance parameter regulator. The proposed approach is suitable for a wheel-legged humanoid robot to manage balance through torque control at joints and regulate force-based interaction on rough terrains. The performance of the proposed RTAF is evaluated on variable gradient slopes and grassland which are the typical rough-terrain scenarios for real-world applications. The experimental results reveal that the maximum speed of grassland movement can reach 3 km/h.
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Benyeogor, Mbadiwe S., Oladayo O. Olakanmi, and Sushant Kumar. "Design of Quad-Wheeled Robot for Multi-Terrain Navigation." Scientific Review, no. 62 (February 5, 2020): 14–22. http://dx.doi.org/10.32861/sr.62.14.22.
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Wheeled robots are often utilized for various remote sensing and telerobotic applications because of their ability to navigate through dynamic environments, mostly under the partial control of a human operator. To make these robots capable to traverse through terrains of rough and uneven topography, their driving mechanisms and controllers must be very efficient at producing and controlling large mechanical power with great precision in real-time, however small the robot may be. This paper discusses an approach for designing a quad-wheeled robot, which is wirelessly controlled with a personal computer (PC) by medium-range radio frequency (RF) transceiver, to navigate through unpaved paths with little or no difficulty. An efficient servo-controlled Ackerman steering mechanism and a high-torque driving power-train were developed. The robot’s controller is programmed to receive and respond to RF control signals from the PC to perform the desired motions. The dynamics of the robot’s drivetrain is modeled and analyzed on MATLAB to predict its performances. The robot was tested on various topographies to determine its physical capabilities. Results show that the robot is capable of non-holonomically constrained motions on rough and uneven terrains.
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Jia, Yan, Xiao Luo, Baoling Han, Guanhao Liang, Jiaheng Zhao, and Yuting Zhao. "Stability Criterion for Dynamic Gaits of Quadruped Robot." Applied Sciences 8, no. 12 (November 25, 2018): 2381. http://dx.doi.org/10.3390/app8122381.
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Dynamic-stability criteria are crucial for robot’s motion planning and balance recovery. Nevertheless, few studies focus on the motion stability of quadruped robots with dynamic gait, none of which have accurately evaluated the robots’ stability. To fill the gaps in this field, this paper presents a new stability criterion for the motion of quadruped robots with dynamic gaits running over irregular terrain. The traditional zero-moment point (ZMP) is improved to analyze the motion on irregular terrain precisely for dynamic gaits. A dynamic-stability criterion and measurement are proposed to determine the stability state of the robot and to evaluate its stability. The simulation results show the limitations of the existing stability criteria for dynamic gaits and indicate that the criterion proposed in this paper can accurately and efficiently evaluate the stability of a quadruped robot using such gaits.
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Mamiya, Shotaro, Shigenori Sano, and Naoki Uchiyama. "Foot Structure with Divided Flat Soles and Springs for Legged Robots and Experimental Verification." Journal of Robotics and Mechatronics 28, no. 6 (December 20, 2016): 799–807. http://dx.doi.org/10.20965/jrm.2016.p0799.
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[abstFig src='/00280006/03.jpg' width='300' text='Robotic foot adaptable to rough terrain' ] Practical ambulation must be realized by walking robots to enable social and industrial support by walking robots in human living environments. A four-legged robot that walks through rough terrain effectively does not erase the fact that most legged robots – particularly biped robots – have difficulty negotiating rough terrain. We focus below on a foot structure and landing control for enabling any type of legged robot to walk through rough terrain. When a walking robot lands on the ground, it is difficult to detect the detailed geometry and dynamic properties of the ground surface. The new foot structure we propose adapts to ground surfaces that have different geometries and hardness. The foot has four-part flat soles. The landing controller we apply to a robot with our proposed foot structure increases the stability of contact with the ground. We verify the effectiveness of our proposed foot structure in experiments.
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Luneckas, Mindaugas, Tomas Luneckas, and Dainius Udris. "Leg placement algorithm for foot impact force minimization." International Journal of Advanced Robotic Systems 15, no. 1 (January 1, 2018): 172988141775151. http://dx.doi.org/10.1177/1729881417751512.
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Walking is considered to be a rather complicated task for autonomous robots. Sustaining dynamic stability, adopting different gaits, and calculating correct foot placement are a necessity to overcome irregular terrain, various environments and completing a range of assignments. Besides that, certain assignments require that robots have to walk on fragile surfaces without damaging it. Furthermore, under some other circumstances, if walking is careless, robots could suffer damage caused by the impact of the terrain. Foot placement, leg motion speed must be controlled to avoid braking surface or even sensors on robot’s feet. In this article, a simple leg placement algorithm is proposed that controls hexapod robot’s leg speed. Thus, force dependence on leg motion speed and step height has been measured by using a piezoelectric sensor. Then, by using leg placement algorithm, we show that the reduction of the impact force between robot’s foot and surface is possible. Using this algorithm, robot feet’s impact force with the surface can be minimized to almost 0 N.
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Bekhti, Mohammed Abdessamad, and Yuichi Kobayashi. "Regressed Terrain Traversability Cost for Autonomous Navigation Based on Image Textures." Applied Sciences 10, no. 4 (February 11, 2020): 1195. http://dx.doi.org/10.3390/app10041195.
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The exploration of remote, unknown, rough environments by autonomous robots strongly depends on the ability of the on-board system to build an accurate predictor of terrain traversability. Terrain traversability prediction can be made more cost efficient by using texture information of 2D images obtained by a monocular camera. In cases where the robot is required to operate on a variety of terrains, it is important to consider that terrains sometimes contain spiky objects that appear as non-uniform in the texture of terrain images. This paper presents an approach to estimate the terrain traversability cost based on terrain non-uniformity detection (TNUD). Terrain images undergo a multiscale analysis to determine whether a terrain is uniform or non-uniform. Terrains are represented using a texture and a motion feature computed from terrain images and acceleration signal, respectively. Both features are then combined to learn independent Gaussian Process (GP) predictors, and consequently, predict vibrations using only image texture features. The proposed approach outperforms conventional methods relying only on image features without utilizing TNUD.
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Xu, He, X. Z. Gao, Yan Xu, Kaifeng Wang, Hongpeng Yu, Zhen Li, Khalil Alipour, and Ozoemena Anthony Ani. "Continuous mobility of mobile robots with a special ability for overcoming driving failure on rough terrain." Robotica 35, no. 10 (August 31, 2016): 2076–96. http://dx.doi.org/10.1017/s0263574716000606.
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SUMMARYFor wheeled mobile robots moving in rough terrains or uncertain environments, driving failure will be encountered when trafficability failure occurs. Continuous mobility of mobile robots with special ability for overcoming driving failure on rough terrain has rarely been considered. This study was conducted using a four-wheel-steering and four-wheel-driving mobile robot equipped with a binocular visual system. First, quasi-static force analysis is carried out to understand the effects of different driving-failure modes on the mobile robot while moving on rough terrain. Secondly, to make the best of the rest of the driving force, robot configuration transformation is employed to select the optimal configuration that can overcome the driving failure. Thirdly, sliding mode control based on back-stepping is adopted to enable the robot achieve continuous trajectory tracking with visual feedback. Finally, the efficacy of the presented approach is verified by simulations and experiments.
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Zong, Chengguo, Zhijian Ji, Junzhi Yu, and Haisheng Yu. "An angle-changeable tracked robot with human-robot interaction in unstructured environments." Assembly Automation 40, no. 4 (April 17, 2020): 565–75. http://dx.doi.org/10.1108/aa-11-2018-0231.
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Purpose The purpose of this paper is to study the adaptability of the tracked robot in complex working environment. It proposes an angle-changeable tracked robot with human–robot interaction in unstructured environment. The study aims to present the mechanical structure and human–robot interaction control system of the tracked robot and analyze the static stability of the robot working in three terrains, i.e. rugged terrain, sloped terrain and stairs. Design/methodology/approach The paper presents the mechanical structure and human–robot interaction control system of the tracked robot. To prevent the detachment of the tracks during obstacle navigation, a new type of passively adaptive device based on the relationship between the track’s variable angle and the forces is presented. Then three types of rough terrain are chosen to analyze the static stability of the tracked robot, i.e. rugged terrain, sloped terrain and stairs. Findings This paper provides the design method of the tracked robot. Owing to its appropriate dimensions, good mass distribution and limited velocity, the tracked robot remains stable on the complex terrains. The experimental results verify the effectiveness of the design method. Originality/value The theoretical analysis of this paper provides basic reference for the structural design of tracked robots.
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Conduraru Slatineanu, Alina, Ioan Doroftei, and Ionel Conduraru. "Design and Kinematic Aspects of a Hybrid Locomotion Robot." Advanced Materials Research 1036 (October 2014): 764–69. http://dx.doi.org/10.4028/www.scientific.net/amr.1036.764.
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Comparing to wheeled robots, legged ones are more flexible and mobile on difficult terrain, where wheeled robots cannot go. Wheels excel on flat surfaces or specially prepared surfaces, where wheeled robots are faster than legged machines. Also, wheeled platforms have simpler mechanical architecture and control algorithms. But they do not perform well when terrain is uneven, which is the case in real life, legged robots becoming more interesting to research and explore. Hybrid locomotion systems were developed to exploit the terrain adaptability of legs in rough terrain and simpler control as well as high speed associated with wheels. In this paper some information about the mechanical design and kinematics of a small hybrid locomotion robot are presented.
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Zhuang, Hongchao, Jiaju Wang, Ning Wang, Weihua Li, Nan Li, Bo Li, and Lei Dong. "A Review of Foot–Terrain Interaction Mechanics for Heavy-Duty Legged Robots." Applied Sciences 14, no. 15 (July 26, 2024): 6541. http://dx.doi.org/10.3390/app14156541.
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Heavy-duty legged robots have played an important role in material transportation, planet exploration, and other fields due to their unique advantages in complex and harsh terrain environments. The instability phenomenon of the heavy-duty legged robots often arises during the dynamic interactions between the supporting feet and the intricate terrains, which significantly impact the ability of the heavy-duty legged robots to move rapidly and accomplish tasks. Therefore, it is necessary to assess the mechanical behavior of foot–terrain interactions for the heavy-duty legged robots. In order to achieve the above goal, a systematic literature review methodology is employed to examine recent technical scientific publications, aiming to identify both current and prospective research fields. The characteristics of supporting feet for different heavy-duty legged robots are compared and analyzed. The foot–terrain mechanical models of the heavy-duty legged robots are discussed. The problems that need further research are summarized and presented, which is conducive to further deepening and expanding the research on the mechanical behavior of foot–terrain interactions for heavy-duty legged robots.
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Zhang, Yilin, Jiayu Zeng, Huimin Sun, Honglin Sun, and Kenji Hashimoto. "Dual-Layer Reinforcement Learning for Quadruped Robot Locomotion and Speed Control in Complex Environments." Applied Sciences 14, no. 19 (September 26, 2024): 8697. http://dx.doi.org/10.3390/app14198697.
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Walking robots have been widely applied in complex terrains due to their good terrain adaptability and trafficability. However, in some environments (such as disaster relief, field navigation, etc.), although a single strategy can adapt to various environments, it is unable to strike a balance between speed and stability. Existing control schemes like model predictive control (MPC) and traditional incremental control can manage certain environments. However, they often cannot balance speed and stability well. These methods usually rely on a single strategy and lack adaptability for dynamic adjustment to different terrains. To address this limitation, this paper proposes an innovative double-layer reinforcement learning algorithm. This algorithm combines Deep Double Q-Network (DDQN) and Proximal Policy Optimization (PPO), leveraging their complementary strengths to achieve both fast adaptation and high stability in complex terrains. This algorithm utilizes terrain information and the robot’s state as observations, determines the walking speed command of the quadruped robot Unitree Go1 through DDQN, and dynamically adjusts the current walking speed in complex terrains based on the robot action control system of PPO. The speed command serves as a crucial link between the robot’s perception and movement, guiding how fast the robot should walk depending on the environment and its internal state. By using DDQN, the algorithm ensures that the robot can set an appropriate speed based on what it observes, such as changes in terrain or obstacles. PPO then executes this speed, allowing the robot to navigate in real time over difficult or uneven surfaces, ensuring smooth and stable movement. Then, the proposed model is verified in detail in Isaac Gym. Wecompare the distances walked by the robot using six different control methods within 10 s. The experimental results indicate that the method proposed in this paper demonstrates excellent speed adjustment ability in complex terrains. On the designed test route, the quadruped robot Unitree Go1 can not only maintain a high walking speed but also maintain a high degree of stability when switching between different terrains. Ouralgorithm helps the robot walk 25.5 m in 10 s, outperforming other methods.
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Luneckas, Mindaugas, Tomas Luneckas, Jonas Kriaučiūnas, Dainius Udris, Darius Plonis, Robertas Damaševičius, and Rytis Maskeliūnas. "Hexapod Robot Gait Switching for Energy Consumption and Cost of Transport Management Using Heuristic Algorithms." Applied Sciences 11, no. 3 (February 2, 2021): 1339. http://dx.doi.org/10.3390/app11031339.
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Due to the prospect of using walking robots in an impassable environment for tracked or wheeled vehicles, walking locomotion is one of the most remarkable accomplishments in robotic history. Walking robots, however, are still being deeply researched and created. Locomotion over irregular terrain and energy consumption are among the major problems. Walking robots require many actuators to cross different terrains, leading to substantial consumption of energy. A robot must be carefully designed to solve this problem, and movement parameters must be correctly chosen. We present a minimization of the hexapod robot’s energy consumption in this paper. Secondly, we investigate the reliance on power consumption in robot movement speed and gaits along with the Cost of Transport (CoT). To perform optimization of the hexapod robot energy consumption, we propose two algorithms. The heuristic algorithm performs gait switching based on the current speed of the robot to ensure minimum energy consumption. The Red Fox Optimization (RFO) algorithm performs a nature-inspired search of robot gait variable space to minimize CoT as a target function. The algorithms are tested to assess the efficiency of the hexapod robot walking through real-life experiments. We show that it is possible to save approximately 7.7–21% by choosing proper gaits at certain speeds. Finally, we demonstrate that our hexapod robot is one of the most energy-efficient hexapods by comparing the CoT values of various walking robots.
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Bae, Junseong, Myeongjin Kim, Bongsub Song, Maolin Jin, and Dongwon Yun. "Snake Robot with Driving Assistant Mechanism." Applied Sciences 10, no. 21 (October 24, 2020): 7478. http://dx.doi.org/10.3390/app10217478.
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Snake robots are composed of multiple links and joints and have a high degree of freedom. They can perform various motions and can overcome various terrains. Snake robots need additional driving algorithms and sensors that acquire terrain data in order to overcome rough terrains such as grasslands and slopes. In this study, we propose a driving assistant mechanism (DAM), which assists locomotion without additional driving algorithms and sensors. In this paper, we confirmed that the DAM prevents a roll down on a slope and increases the locomotion speed through dynamic simulation and experiments. It was possible to overcome grasslands and a 27 degrees slope without using additional driving controllers. In conclusion, we expect that a snake robot can conduct a wide range of missions well, such as exploring disaster sites and rough terrain, by using the proposed mechanism.
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Chen, Liuhongxu, Ping Du, Pengfei Zhan, and Bo Xie. "Gait Learning for Hexapod Robot Facing Rough Terrain Based on Dueling-DQN Algorithm." International Journal of Computer Science and Information Technology 2, no. 1 (March 25, 2024): 408–24. http://dx.doi.org/10.62051/ijcsit.v2n1.44.
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In the handling of dangerous goods in explosive environments, robots are increasingly being used instead of human operators. Robots designed for operation in explosive environments are mostly equipped with tracked structures, which, due to their limited terrain adaptability, struggle to movement rugged landscapes. Hexapod robots, with their excellent maneuverability and adaptability, possess advantages in completing hazardous material handling tasks in such rugged terrains. One current challenge lies in enabling hexapod robots to autonomously adjust their gaits to cope with rugged terrain. This paper proposes a gait learning method based on the Dueling Deep Q-Network (Dueling-DQN) algorithm to address the gait adjustment problem of hexapod robots in sloped, terraced, and rugged terrain. The method combines lidar data and reinforcement learning to extract features from the lidar data to determine terrain types and foot coordinates. Finally, the Dueling-DQN algorithm and redundant phase strategy are employed to facilitate the motion of hexapod robots in these three types of rugged terrain. Simulation and prototype experiments are conducted to evaluate the Dueling-DQN algorithm's performance in terms of rewards and stability margins for the three types of terrain. During algorithm training on sloped, terraced, and rugged terrain, stable rewards and positive stability margins are achieved after approximately 490 iterations. The effectiveness and feasibility of the proposed method are further validated through Gazebo simulation and prototype experiments. In the context of movement in rugged terrain within explosive environments, the gait learning method based on the Dueling-DQN algorithm offers valuable insights into the control of hexapod robots.
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Žák, Marek, Jaroslav Rozman, and František V. Zbořil. "Energy Efficiency of a Wheeled Bio-Inspired Hexapod Walking Robot in Sloping Terrain." Robotics 12, no. 2 (March 15, 2023): 42. http://dx.doi.org/10.3390/robotics12020042.
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Multi-legged robots, such as hexapods, have great potential to navigate challenging terrain. However, their design and control are usually much more complex and energy-demanding compared to wheeled robots. This paper presents a wheeled six-legged robot with five degrees of freedom, that is able to move on a flat surface using wheels and switch to gait in rugged terrain, which reduces energy consumption. The novel joint configuration mimics the structure of insect limbs and allows our robot to overcome difficult terrain. The wheels reduce energy consumption when moving on flat terrain and the trochanter joint reduces energy consumption when moving on slopes, extending the operating time and range of the robot. The results of experiments on sloping terrain are presented. It was confirmed that the use of the trochanter joint can lead to a reduction in energy consumption when moving in sloping terrain.
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Ranjan, Rahul, Seungjae Lee, and Joongeup Kye. "Design of Tactical Multipurpose All–Terrain Mobile Robot." International Journal of Membrane Science and Technology 10, no. 2 (October 20, 2023): 2224–37. http://dx.doi.org/10.15379/ijmst.v10i2.2799.
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Robotic systems that can assist soldiers in dangerous operations are essential technologies that improve safety and efficiency. This paper presents the development of mobile robots that can assist soldiers with target acquisition and surveillance on the battlefield. We have built a prototype that incorporates a firearm system that can perform shooting tasks. Our aim is to design and develop mobile robots that can cope with challenging scenarios such as terrorist attacks and rescue missions. Our robots can transmit photographic images or live streaming via a spy camera, and based on the visual information, soldiers can fire at their target with a gun mounted on the robot. We discussed the key technologies needed to design and manufacture such combat robots. Existing high-specification combat robots are expensive and inaccessible to students and researchers interested in the field. This paper reveals the technologies to develop a low-cost combat robot. This system can be used for real-time applications at an affordable price by using more advanced technology to improve its accuracy.
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Zha, Fusheng, Chen Chen, Wei Guo, Penglong Zheng, and Junyi Shi. "A free gait controller designed for a heavy load hexapod robot." Advances in Mechanical Engineering 11, no. 3 (March 2019): 168781401983836. http://dx.doi.org/10.1177/1687814019838369.
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As macroscopic rough terrains are time varying and full of local topographic mutations, stable locomotions of legged robots moving through such terrains in a fixed gait form can be hardly obtained. This problem becomes more severe as the size and weight of the robot increase. An ideal pre-planned gait changing method can also be hardly designed due to the same limitations. Aiming to solve the problem, a new kind of free gait controller applied to a large-scale hexapod robot with heavy load is developed. The controller consists of two parts, a free gait planner and a gait regulator. Based on the observed macro terrain changes, the free gait planner adopts the macro terrain recognition method and the status searching method for selecting the best leg support status automatically. The gait regulator is adopted for the correction of the selected status to cope with local topographic mutations. Detailed simulation experiments are presented to demonstrate that, with the designed controller, the adopted hexapod robot can change moving gaits automatically in terms of the terrain conditions and obtain stable locomotions through rough terrains.
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Luneckas, Tomas. "EVALUATING TERRAIN IRREGULARITY BY ROBOT POSTURE / PAVIRŠIAUS NETOLYGUMO VERTINIMAS PAGAL ROBOTO PADĖTĮ." Mokslas - Lietuvos ateitis 3, no. 1 (August 22, 2011): 96–99. http://dx.doi.org/10.3846/mla.2011.020.
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A method that allows evaluate the terrain irregularity by a robot posture is presented in this paper. A necessity to evaluate terrain irregularity is pointed out. Description of irregular terrain is given. A possibility to evaluate terrain irregularity by feet coordinate standard deviation is proposed. When deviation is σ = 0, all robots are in one plane. Bigger σ the more robot legs are scattered, meaning robot is walking in irregular terrain. A method to evaluate robot horizontality according to terrain by position of three planes is introduced.
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Olivier Akansie, Kouame Yann, Rajashekhar C. Biradar, Karthik Rajendra, and Geetha D. Devanagavi. "A terrain data collection sensor box towards a better analysis of terrains conditions." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (December 1, 2024): 4388. http://dx.doi.org/10.11591/ijai.v13.i4.pp4388-4402.
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<p>Autonomous mobile robots are increasingly used across various applications, relying on multiple sensors for environmental awareness and efficient task execution. Given the unpredictability of human environments, versatility is crucial for these robots. Their performance is largely determined by how they perceive their surroundings. This paper introduces a machine learning (ML) approach focusing on land conditions to enhance a robot’s locomotion. The authors propose a method to classify terrains for data collection, involving the design of an apparatus to gather field data. This design is validated by correlating collected data with the output of a standard ML model for terrain classification. Experiments show that the data from this apparatus improves the accuracy of the ML classifier, highlighting the importance of including such data in the dataset.</p>
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Mrva, Jakub, Martin Stejskal, and Jan Faigl. "ON TRAVERSABILITY COST EVALUATION FROM PROPRIOCEPTIVE SENSING FOR A CRAWLING ROBOT." Acta Polytechnica CTU Proceedings 2, no. 2 (December 31, 2015): 34–39. http://dx.doi.org/10.14311/app.2015.1.0034.
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Traversability characteristics of the robot working environment are crucial in planning an efficient path for a robot operating in rough unstructured areas. In the literature, approaches to wheeled or tracked robots can be found, but a relatively little attention is given to walking multi-legged robots. Moreover, the existing approaches for terrain traversability assessment seem to be focused on gathering key features from a terrain model acquired from range data or camera image and only occasionally supplemented with proprioceptive sensing that expresses the interaction of the robot with the terrain. This paper addresses the problem of traversability cost evaluation based on proprioceptive sensing for a hexapod walking robot while optimizing different criteria. We present several methods of evaluating the robot-terrain interaction that can be used as a cost function for an assessment of the robot motion that can be utilized in high-level path-planning algorithms.
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Conduraru Slatineanu, Alina, Ioan Doroftei, Ionel Conduraru, and Dorin Luca. "Hexapod Locomotion of a Leg-Wheel Hybrid Mobile Robot." Applied Mechanics and Materials 658 (October 2014): 581–86. http://dx.doi.org/10.4028/www.scientific.net/amm.658.581.
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Legged vehicles are more flexible and mobile on difficult terrain, comparing to wheeled robots. Wheels are convenient on flat surfaces or specially prepared surfaces, wheeled vehicles being faster than legged ones. Also, wheeled robots are simpler in terms of mechanical architecture and control algorithms. But they do not perform well on uneven terrain, which is the case in real life, legged robots becoming more interesting to research and explore. This is why hybrid locomotion systems have been developed, in order to exploit the terrain adaptability of legs in rough terrain and simpler control as well as high speed associated with wheels. In this paper some design and kinematic aspects as well as hexapod locomotion of a small hybrid robot are presented.
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Zhao, Kai, Mingming Dong, and Liang Gu. "A New Terrain Classification Framework Using Proprioceptive Sensors for Mobile Robots." Mathematical Problems in Engineering 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/3938502.
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Mobile robots that operate in real-world environments interact with the surroundings to generate complex acoustics and vibration signals, which carry rich information about the terrain. This paper presents a new terrain classification framework that utilizes both acoustics and vibration signals resulting from the robot-terrain interaction. As an alternative to handcrafted domain-specific feature extraction, a two-stage feature selection method combining ReliefF and mRMR algorithms was developed to select optimal feature subsets that carry more discriminative information. As different data sources can provide complementary information, a multiclassifier combination method was proposed by considering a priori knowledge and fusing predictions from five data sources: one acoustic data source and four vibration data sources. In this study, four conceptually different classifiers were employed to perform the classification, each with a different number of optimal features. Signals were collected using a tracked robot moving at three different speeds on six different terrains. The new framework successfully improved classification performance of different classifiers using the newly developed optimal feature subsets. The greater improvement was observed for robot traversing at lower speeds.
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Bazeille, Stéphane, Jesus Ortiz, Francesco Rovida, Marco Camurri, Anis Meguenani, Darwin G. Caldwell, and Claudio Semini. "Active camera stabilization to enhance the vision of agile legged robots." Robotica 35, no. 4 (November 17, 2015): 942–60. http://dx.doi.org/10.1017/s0263574715000909.
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SUMMARYLegged robots have the potential to navigate in more challenging terrains than wheeled robots. Unfortunately, their control is more demanding, because they have to deal with the common tasks of mapping and path planning as well as more specific issues of legged locomotion, like balancing and foothold planning. In this paper, we present the integration and the development of a stabilized vision system on the fully torque-controlled hydraulically actuated quadruped robot (HyQ). The active head added onto the robot is composed of a fast pan and tilt unit (PTU) and a high-resolution wide angle stereo camera. The PTU enables camera gaze shifting to a specific area in the environment (both to extend and refine the map) or to track an object while navigating. Moreover, as the quadruped locomotion induces strong regular vibrations, impacts or slippages on rough terrain, we took advantage of the PTU to mechanically compensate for the robot's motions. In this paper, we demonstrate the influence of legged locomotion on the quality of the visual data stream by providing a detailed study of HyQ's motions, which are compared against a rough terrain wheeled robot of the same size. Our proposed Inertial Measurement Unit (IMU)-based controller allows us to decouple the camera from the robot motions. We show through experiments that, by stabilizing the image feedback, we can improve the onboard vision-based processes of tracking and mapping. In particular, during the outdoor tests on the quadruped robot, the use of our camera stabilization system improved the accuracy on the 3D maps by 25%, with a decrease of 50% of mapping failures.
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Gao, Xin’an, Xiaorong Guan, Yanlong Yang, and Jingmin Zhang. "Design and Ground Performance Evaluation of a Multi-Joint Wheel-Track Composite Mobile Robot for Enhanced Terrain Adaptability." Applied Sciences 13, no. 12 (June 18, 2023): 7270. http://dx.doi.org/10.3390/app13127270.
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The tracked-wheeled mobile robot has gained significant attention in military, agricultural, construction, and other fields due to its exceptional mobility and off-road capabilities. Therefore, it is an ideal choice for reconnaissance and exploration tasks. In this study, we proposed a multi-jointed tracked-wheeled compound mobile robot that can overcome various terrains and obstacles. Based on the characteristics of multi-jointed robots, we designed two locomotion modes for the robot to climb stairs and established the kinematics/dynamics equations for its land movement. We evaluated the robot’s stability during slope climbing, its static stability during stair climbing, and its ability to cross trenches. Based on our evaluation results, we determined the key conditions for the robot to overcome obstacles, the maximum height it can climb stairs, and the maximum width it can cross trenches. Additionally, we developed a simulation model to verify the robot’s performance in different terrains and the reliability of its stair-climbing gait. The simulation results demonstrate that our multi-jointed tracked-wheeled compound mobile robot exhibits excellent reliability and adaptability in complex terrain, indicating broad application prospects in various fields and space missions.
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Zheng, Qingyuan, Yu Tian, Yang Deng, Xianjin Zhu, Zhang Chen, and Bing Liang. "Reinforcement Learning-Based Control of Single-Track Two-Wheeled Robots in Narrow Terrain." Actuators 12, no. 3 (February 28, 2023): 109. http://dx.doi.org/10.3390/act12030109.
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The single-track two-wheeled (STTW) robot has the advantages of small size and flexibility, and it is suitable for traveling in narrow terrains of mountains and jungles. In this article, a reinforcement learning control method for STTW robots is proposed for driving fast in narrow terrain with limited visibility and line-of-sight occlusions. The proposed control scheme integrates path planning, trajectory tracking, and balancing control in a single framework. Based on this method, the state, action, and reward function are defined for narrow terrain passing tasks. At the same time, we design the actor network and the critic network structures and use the twin delayed deep deterministic policy gradient (TD3) to train these neural networks to construct a controller. Next, a simulation platform is formulated to test the performances of the proposed control method. The simulation results show that the obtained controller allows the STTW robot to effectively pass the training terrain, as well as the four test terrains. In addition, this article conducts a simulation comparison to prove the advantages of the integrated framework over traditional methods and the effectiveness of the reward function.
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Zhu, Yaguang, Kailu Luo, Chao Ma, Qiong Liu, and Bo Jin. "Superpixel Segmentation Based Synthetic Classifications with Clear Boundary Information for a Legged Robot." Sensors 18, no. 9 (August 25, 2018): 2808. http://dx.doi.org/10.3390/s18092808.
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In view of terrain classification of the autonomous multi-legged walking robots, two synthetic classification methods for terrain classification, Simple Linear Iterative Clustering based Support Vector Machine (SLIC-SVM) and Simple Linear Iterative Clustering based SegNet (SLIC-SegNet), are proposed. SLIC-SVM is proposed to solve the problem that the SVM can only output a single terrain label and fails to identify the mixed terrain. The SLIC-SegNet single-input multi-output terrain classification model is derived to improve the applicability of the terrain classifier. Since terrain classification results of high quality for legged robot use are hard to gain, the SLIC-SegNet obtains the satisfied information without too much effort. A series of experiments on regular terrain, irregular terrain and mixed terrain were conducted to present that both superpixel segmentation based synthetic classification methods can supply reliable mixed terrain classification result with clear boundary information and will put the terrain depending gait selection and path planning of the multi-legged robots into practice.
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Yang, Kuo, Xinhui Liu, Changyi Liu, and Ziwei Wang. "Motion-Control Strategy for a Heavy-Duty Transport Hexapod Robot on Rugged Agricultural Terrains." Agriculture 13, no. 11 (November 11, 2023): 2131. http://dx.doi.org/10.3390/agriculture13112131.
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Legged agricultural transportation robots are efficient tools that can autonomously transport goods over agricultural terrain, and their introduction helps to improve the efficiency and quality of agricultural production. Their effectiveness depends on their adaptability to different environmental conditions, which is especially true for heavy-duty robots that exert ground forces. Therefore, this study proposes a motion-control strategy for a heavy-duty transport hexapod robot. Two critical tasks were accomplished in this paper: (1) estimating the support surface angle based on the robot’s foot position and body posture, and accordingly determining the motion constraint conditions on this support surface and the body posture based on energy optimization; (2) proposing an adaptive fuzzy impedance algorithm for real-time force–position composite control for adjusting foot position, in order to reduce the steady-state force tracking error caused by terrain stiffness, thus ensuring body stability through tracking of variable foot-end forces. An element of hardware in the loop control platform for a 3.55-ton device was designed and compared with the current popular force-control methods under different external contact terrains. The results show that the proposed control method can effectively reduce force errors, establish support forces faster on less-stiff environments, and reduce the torso tilt during phase switching.
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Rafeeq, Mohammed, Siti Fauziah Toha, Salmiah Ahmad, Mohd Asyraf Razib, Ahmad Syahrin Idris, and Mohammad Osman Tokhi. "Amphibious Robots Locomotion Strategies in Unstructured Complex Environments: A Review." Platform : A Journal of Engineering 8, no. 1 (March 30, 2024): 12. http://dx.doi.org/10.61762/pajevol8iss1art26197.
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In the previous literature, amphibious robots focused mainly on locomotion in underwater and flat land surface manoeuvring. Few amphibious robots focused on unstructured land environments. The amphibious robot designs were more emphasised in academics, leading to more work done in building amphibious robots that mimic biological amphibians, imitating the geometry and overall functionality of the amphibious robots. Developing amphibious robots with propulsive mechanisms for manoeuvring in a water environment received more attention than other functionalities like adaptability on rough natural terrain and obstacle repositioning capability. However, practical applications like reconnaissance and surveying posed challenges in the ground environment, which had unstructured and complex terrain profiles, especially in the transition area. Therefore, reviewing the amphibious robots focused on manoeuvring complex uneven surfaces was essential. The literature had comprehensive review papers on navigation strategies encompassing manoeuvring on flat ground surfaces and underwater locomotion. There was a need for a focused study that highlighted the amphibious robot that manoeuvred in an unstructured land environment. The open challenges and recent solutions by designing new mechanisms and deployment issues were highlighted and reviewed. Hence, the paper addressed a more specific review of amphibious robot locomotion in an unstructured environment. The paper also discussed a case study of an amphibious robot capable of locomotion in unstructured environments. It was envisaged that the review would provide directions and insights to researchers and robotic system designers on developing robust propulsive mechanisms for amphibious robots capable of locomotion in unstructured environments.Keywords: manoeuvrability, mobility, terrain, unified locomotion, mechanism, propulsion
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Nakajima, Shuro. "RT-Mover: a rough terrain mobile robot with a simple leg–wheel hybrid mechanism." International Journal of Robotics Research 30, no. 13 (June 22, 2011): 1609–26. http://dx.doi.org/10.1177/0278364911405697.
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There is a strong demand in many fields for practical robots, such as a porter robot and a personal mobility robot, that can move over rough terrain while carrying a load horizontally. We have developed a robot, called RT-Mover, which shows adequate mobility performance on targeted types of rough terrain. It has four drivable wheels and two leg-like axles but only five active shafts. A strength of this robot is that it realizes both a leg mode and a wheel mode in a simple mechanism. In this paper, the mechanical design concept is discussed. With an emphasis on minimizing the number of drive shafts, a mechanism is designed for a four-wheeled mobile body that is widely used in practical locomotive machinery. Also, strategies for moving on rough terrain are proposed. The kinematics, stability, and control of RT-Mover are also described in detail. Some typical cases of rough terrain for wheel mode and leg mode are selected, and the robot’s ability of locomotion is assessed through simulations and experiments. In each case, the robot is able to move over rough terrain while maintaining the horizontal orientation of its platform.
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Huskić, Goran, Sebastian Buck, Matthieu Herrb, Simon Lacroix, and Andreas Zell. "High-speed path following control of skid-steered vehicles." International Journal of Robotics Research 38, no. 9 (July 2019): 1124–48. http://dx.doi.org/10.1177/0278364919859634.
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We present a robust control scheme for skid-steered vehicles that enables high-speed path following on challenging terrains. First, a kinematic model with experimentally identified parameters is constructed to describe the terrain-dependent motion of skid-steered vehicles. Using Lyapunov theory, a nonlinear control law is defined, guaranteeing the convergence of the vehicle to the path. To allow smooth and accurate motion at higher speeds, an additional linear velocity control scheme is proposed, which takes actuator saturation, path following error, and reachable curvatures into account. The combined solution is experimentally evaluated and compared against two state-of-the-art algorithms, by using two different robots on several different terrain types, at different speeds. A Robotnik Summit XL robot is tested on three different terrain types and two different paths at speeds up to [Formula: see text] m/s. A Segway RMP 440 robot is tested on three different terrain types and two different path types at speeds up to [Formula: see text] m/s.
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Pookkuttath, Sathian, Raihan Enjikalayil Abdulkader, Mohan Rajesh Elara, and Prabakaran Veerajagadheswar. "AI-Enabled Vibrotactile Feedback-Based Condition Monitoring Framework for Outdoor Mobile Robots." Mathematics 11, no. 18 (September 5, 2023): 3804. http://dx.doi.org/10.3390/math11183804.
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An automated Condition Monitoring (CM) and real-time controlling framework is essential for outdoor mobile robots to ensure the robot’s health and operational safety. This work presents a novel Artificial Intelligence (AI)-enabled CM and vibrotactile haptic-feedback-based real-time control framework suitable for deploying mobile robots in dynamic outdoor environments. It encompasses two sections: developing a 1D Convolutional Neural Network (1D CNN) model for predicting system degradation and terrain flaws threshold classes and a vibrotactile haptic feedback system design enabling a remote operator to control the robot as per predicted class feedback in real-time. As vibration is an indicator of failure, we identified and separated system- and terrain-induced vibration threshold levels suitable for CM of outdoor robots into nine classes, namely Safe, moderately safe system-generated, and moderately safe terrain-induced affected by left, right, and both wheels, as well as severe classes such as unsafe system-generated and unsafe terrain-induced affected by left, right, and both wheels. The vibration-indicated data for each class are modelled based on two sensor data: an Inertial Measurement Unit (IMU) sensor for the change in linear and angular motion and a current sensor for the change in current consumption at each wheel motor. A wearable novel vibrotactile haptic feedback device architecture is presented with left and right vibration modules configured with unique haptic feedback patterns corresponding to each abnormal vibration threshold class. The proposed haptic-feedback-based CM framework and real-time remote controlling are validated with three field case studies using an in-house-developed outdoor robot, resulting in a threshold class prediction accuracy of 91.1% and an effectiveness that, by minimising the traversal through undesired terrain features, is four times better than the usual practice.
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SZABARI, MIKULAS, and RADEK KNOFLICEK. "LEGGED ROBOT LOCOMOTION IN RESISTIVE TERRAIN: A COMPARISON OF TWO METHODS." MM Science Journal 2022, no. 4 (November 16, 2022): 6040–48. http://dx.doi.org/10.17973/mmsj.2022_11_2022047.
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Every day new technology appears. In the field of legged robots, it is not otherwise. LIDAR vision, artificial intelligence, computing power and new drives help to improve the state of legged robots. One of the unsolved problems is still terrain navigation, such as locomotion in resistive terrain. To determine the correct locomotion, research must be carried out. In this paper, we compare two different situations, where a six-legged robot walks through variable resistive terrain with different gait in the first one and with different leg trajectories in the swing phase in the second one.
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Dong, Yunlong, Wei Guo, Fusheng Zha, Yizhou Liu, Chen Chen, and Lining Sun. "A Vision-Based Two-Stage Framework for Inferring Physical Properties of the Terrain." Applied Sciences 10, no. 18 (September 17, 2020): 6473. http://dx.doi.org/10.3390/app10186473.
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The friction and stiffness properties of the terrain are very important pieces of information for mobile robots in motion control, dynamics parameter adjustment, trajectory planning, etc. Inferring the friction and stiffness properties in advance can improve the safety, adaptability and reliability, and reduce the energy consumption of the robot. This paper proposes a vision-based two-stage framework for pre-estimating physical properties of the terrain. We established a field terrain image dataset with weak annotations. A semantic segmentation network that can segment terrains at the pixel level was designed. Given that the same terrain also has different physical properties, we designed two kinds of image features, and we use a decision-making model to realize the mapping from terrain to physical properties. We trained and tested the network comprehensively, and experimented with the complete framework for estimating physical properties. The experimental results show that our framework has good performance.
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Marín Arciniegas, Jairo José, and Oscar Andrés Vivas Albán. "Design and Construction of a Snake-Like Robot Implementing Rectilinear and Sidewinding Gait Motions." TecnoLógicas 26, no. 56 (December 6, 2022): e2412. http://dx.doi.org/10.22430/22565337.2412.
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Bio-inspired robots offer locomotion versatility in a wide variety of terrains that conventional robots cannot access. One such bio-inspired platform is snake-like robots, which are mechanisms designed to move like biological snakes. The aim of this paper was to implement and validate, through comparison in real and simulation tests on flat terrain, the design of a snake robot that allows movements in two perpendicular planes, by the application of three-dimensional locomotion modes. The prototype robot had a modular and sequential architecture composed of eight 3D printed segments. The necessary torques for each motor are found by means of a simulation in Matlab – Simulink and the SimScape tool. The Webots mobile robotics simulator was used to create a parameterized virtual model of the robot, where two types of gaits were programmed: sidewinding and rectilinear. Results showed that the robot undertakes lower than 1 second in execution time to reach the total distance in each of the proposed marches when comparted to the simulation. In addition, mean differences of 6 cm for the distances during the sidewinding mode experiment and 1.2 cm in the deviation in the rectilinear mode on flat terrain were obtained. In conclusion, there is a great similarity between the simulation tests and those performed with the actual robot, and it was also possible to verify that the behavior of the prototype robot is satisfactory over short distances.
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Jeon, Haneul, and Donghun Lee. "Explicit Identification of Pointwise Terrain Gradients for Speed Compensation of Four Driving Tracks in Passively Articulated Tracked Mobile Robot." Mathematics 11, no. 4 (February 10, 2023): 905. http://dx.doi.org/10.3390/math11040905.
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Tracked mobile robots can overcome the limitations of wheeled and legged robots in environments, such as construction and mining, but there are still significant challenges to be addressed in terms of trajectory tracking. This study proposes a kinematic strategy to improve the trajectory-tracking performance of a PASTRo (Passively Articulated Suspension based Track-typed mobile robot), which comprises four tracks, two rockers, a differential gear, and a main body. Due to the difficulties in explicitly identifying track-terrain contact angles, suspension kinematics is used to identify track-terrain contact angles (TTCA) in arbitrarily rough terrains. Thus, the TTCA-based driving velocity projection method is proposed in this study to improve the maneuverability of PASTRo in arbitrarily rough terrains. The RecurDyn-Simulink co-simulator is used to examine the improvement of PASTRo compared to a tracked mobile robot non-suspension version. The results indicate that PASTRo has a 33.3% lower RMS(Root Mean Square) distance error, 56.3% lower RMS directional error, and 43.2% lower RMS offset error than the four-track skid-steer mobile robot (SSMR), even with planar SSMR kinematics. To improve the maneuverability of PASTRo without any information on the rough terrain, the TTCA is calculated from the suspension kinematics, and the TTCA obtained is used for both TTCA-based driving velocity projection methods. The results show that PASTRo, with the TTCA-based driving velocity projection method, has a 39.2% lower RMS distance error, 57.9% lower RMS directional error, and 51.9% lower RMS offset error than the four-track SSMR.
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Li, Yunquan, Yujia Li, Tao Ren, Jiutian Xia, Hao Liu, Changchun Wu, Senyuan Lin, and Yonghua Chen. "An Untethered Soft Robotic Dog Standing and Fast Trotting with Jointless and Resilient Soft Legs." Biomimetics 8, no. 8 (December 8, 2023): 596. http://dx.doi.org/10.3390/biomimetics8080596.
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Soft robots are compliant, impact resistant, and relatively safe in comparison to hard robots. However, the development of untethered soft robots is still a major challenge because soft legs cannot effectively support the power and control systems. Most untethered soft robots apply a crawling or walking gait, which limits their locomotion speed and mobility. This paper presents an untethered soft robot that can move with a bioinspired dynamic trotting gait. The robot is driven by inflatable soft legs designed on the basis of the pre-charged pneumatic (PCP) actuation principle. Experimental results demonstrate that the developed robot can trot stably with the fastest speed of 23 cm/s (0.97 body length per second) and can trot over different terrains (slope, step, rough terrain, and natural terrains). The robotic dog can hold up to a 5.5 kg load in the static state and can carry up to 1.5 kg in the trotting state. Without any rigid components inside the legs, the developed robotic dog exhibits resistance to large impacts, i.e., after withstanding a 73 kg adult (46 times its body mass), the robotic dog can stand up and continue its trotting gait. This innovative robotic system has great potential in equipment inspection, field exploration, and disaster rescue.
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Sokolov, Oleksandr, Aleksander Hosovsky, Vitalii Ivanov, and Ivan Pavlenko. "Movement Monitoring System for a Pneumatic Muscle Actuator." Journal of Engineering Sciences 10, no. 1 (2023): A1—A5. http://dx.doi.org/10.21272/jes.2023.10(1).a1.
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Recent advancements in soft pneumatic robot research have demonstrated these robots’ capability to interact with the environment and humans in various ways. Their ability to move over rough terrain and grasp objects of irregular shape, regardless of position, has garnered significant interest in developing new pneumatic soft robots. Integrating industrial design with related technologies holds great promise for the future, potentially bringing about a new lifestyle and revolutionizing the industry. As robots become increasingly practical, there is a growing need for sensitivity, robustness, and efficiency improvements. It is anticipated that the development of these intelligent pneumatic soft robots will play a critical role in serving the needs of society and production shortly. The present article is concerned with developing a system for monitoring a pneumatic robot’s parameters, including a spatial coordinate system. The focus is on utilizing the relationship between the coordinates and pressure to model the movement of the soft robot within the MATLAB simulation environment.
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Yin, Hao, Ruiqi Shi, and Jiang Liu. "Structural Design and Control Research of Multi-Segmented Biomimetic Millipede Robot." Biomimetics 9, no. 5 (May 11, 2024): 288. http://dx.doi.org/10.3390/biomimetics9050288.
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Due to their advantages of good stability, adaptability, and flexibility, multi-legged robots are increasingly important in fields such as rescue, military, and healthcare. This study focuses on the millipede, a multi-segmented organism, and designs a novel multi-segment biomimetic robot based on an in-depth investigation of the millipede’s biological characteristics and locomotion mechanisms. Key leg joints of millipede locomotion are targeted, and a mathematical model of the biomimetic robot’s leg joint structure is established for kinematic analysis. Furthermore, a central pattern generator (CPG) control strategy is studied for multi-jointed biomimetic millipede robots. Inspired by the millipede’s neural system, a simplified single-loop CPG network model is constructed, reducing the number of oscillators from 48 to 16. Experimental trials are conducted using a prototype to test walking in a wave-like gait, walking with a leg removed, and walking on complex terrain. The results demonstrate that under CPG waveform input conditions, the robot can walk stably, and the impact of a leg failure on overall locomotion is acceptable, with minimal speed loss observed when walking on complex terrain. The research on the structure and motion control algorithms of multi-jointed biomimetic robots lays a technical foundation, expanding their potential applications in exploring unknown environments, rescue missions, agriculture, and other fields.
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Goto, Tomoya, and Genya Ishigami. "CNN-Based Terrain Classification with Moisture Content Using RGB-IR Images." Journal of Robotics and Mechatronics 33, no. 6 (December 20, 2021): 1294–302. http://dx.doi.org/10.20965/jrm.2021.p1294.
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Unmanned mobile robots in rough terrains are a key technology for achieving smart agriculture and smart construction. The mobility performance of robots highly depends on the moisture content of soil, and past few studies have focused on terrain classification using moisture content. In this study, we demonstrate a convolutional neural network-based terrain classification method using RGB-infrared (IR) images. The method first classifies soil types and then categorizes the moisture content of the terrain. A three-step image preprocessing for RGB-IR images is also integrated into the method that is applicable to an actual environment. An experimental study of the terrain classification confirmed that the proposed method achieved an accuracy of more than 99% in classifying the soil type. Furthermore, the classification accuracy of the moisture content was approximately 69% for pumice and 100% for dark soil. The proposed method can be useful for different scenarios, such as small-scale agriculture with mobile robots, smart agriculture for monitoring the moisture content, and earthworks in small areas.
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Chen, Tianxiang, Yipeng Huangfu, Sutthiphong Srigrarom, and Boo Cheong Khoo. "Path Planning and Motion Control of Robot Dog Through Rough Terrain Based on Vision Navigation." Sensors 24, no. 22 (November 15, 2024): 7306. http://dx.doi.org/10.3390/s24227306.
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This article delineates the enhancement of an autonomous navigation and obstacle avoidance system for a quadruped robot dog. Part one of this paper presents the integration of a sophisticated multi-level dynamic control framework, utilizing Model Predictive Control (MPC) and Whole-Body Control (WBC) from MIT Cheetah. The system employs an Intel RealSense D435i depth camera for depth vision-based navigation, which enables high-fidelity 3D environmental mapping and real-time path planning. A significant innovation is the customization of the EGO-Planner to optimize trajectory planning in dynamically changing terrains, coupled with the implementation of a multi-body dynamics model that significantly improves the robot’s stability and maneuverability across various surfaces. The experimental results show that the RGB-D system exhibits superior velocity stability and trajectory accuracy to the SLAM system, with a 20% reduction in the cumulative velocity error and a 10% improvement in path tracking precision. The experimental results also show that the RGB-D system achieves smoother navigation, requiring 15% fewer iterations for path planning, and a 30% faster success rate recovery in challenging environments. The successful application of these technologies in simulated urban disaster scenarios suggests promising future applications in emergency response and complex urban environments. Part two of this paper presents the development of a robust path planning algorithm for a robot dog on a rough terrain based on attached binocular vision navigation. We use a commercial-of-the-shelf (COTS) robot dog. An optical CCD binocular vision dynamic tracking system is used to provide environment information. Likewise, the pose and posture of the robot dog are obtained from the robot’s own sensors, and a kinematics model is established. Then, a binocular vision tracking method is developed to determine the optimal path, provide a proposal (commands to actuators) of the position and posture of the bionic robot, and achieve stable motion on tough terrains. The terrain is assumed to be a gentle uneven terrain to begin with and subsequently proceeds to a more rough surface. This work consists of four steps: (1) pose and position data are acquired from the robot dog’s own inertial sensors, (2) terrain and environment information is input from onboard cameras, (3) information is fused (integrated), and (4) path planning and motion control proposals are made. Ultimately, this work provides a robust framework for future developments in the vision-based navigation and control of quadruped robots, offering potential solutions for navigating complex and dynamic terrains.
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Takuma, Takashi, and Koh Hosoda. "Terrain Negotiation of a Compliant Biped Robot Driven by Antagonistic Artificial Muscles." Journal of Robotics and Mechatronics 19, no. 4 (August 20, 2007): 423–28. http://dx.doi.org/10.20965/jrm.2007.p0423.
Full textAbstract:
Human beings realize adaptive bipedal walking negotiating different terrain, which is still difficult for biped robots driven by electric motors. We developed a biped robot driven by antagonistic artificial muscles that negotiates several types of terrain. Antagonistic muscles combined with a simple feed-forward controller realize joint compliance without a time delay, enabling the robot to adapt to terrain changes and to sense terrain because walking behavior results from interaction between robot dynamics and the terrain. Experimental results demonstrate that the walking cycle changes based on joint compliance and the type of terrain. Using the relationship between the two, the robot regulates its walking cycle by changing its joint compliance. The compliance by such antagonistic muscles is a promising solution for realizing adaptive bipedal walking.